AKI Stages are defined based on KDIGO:
#extract cohort --Table1
# by default, we assume cdm schema is on the same server as current schema,
cohort<-extract_cohort(conn,
remote_CDM=params$remote_CDM,
cdm_db_link=config_file$cdm_db_link,
cdm_db_name=config_file$cdm_db_name,
cdm_db_schema=config_file$cdm_db_schema,
start_date="2010-01-01",
end_date="2018-12-31",
verb=F)
The above codes extracted AKI study cohort based on the “Inclusion” and “Exclusion” criteria specified above. The final output will be automatically saved in the current working directory /d1/home/xsong/AKI_CDM as “Table1.rda”. More details are included in the following consort diagram.
In this section, we will collect variables from PCORNET_CDM tables: DEMOGRAPHIC, ENCOUNTER, VITAL, LAB_RESULT_CM, PRESCRIBING MEDICATION, DIAGNOSIS, PROCEDURE, check data quality and generate variable summaries table by table.
Demographic variables include Age (calculated from Birth_Date), Sex, Race, Ethnicity.
| key | value | ADMIT | AKI1 | AKI2 | AKI3 | NONAKI |
|---|---|---|---|---|---|---|
| Age Group | ||||||
| AGE_GRP | 18-25 | 8061, 6% | 816, 4% | 105, 5% | 75, 5% | 7150, 6% |
| AGE_GRP | 26-35 | 13009, 9% | 1316, 7% | 161, 7% | 129, 8% | 11571, 9% |
| AGE_GRP | 36-45 | 15384, 11% | 1765, 9% | 257, 11% | 200, 13% | 13430, 11% |
| AGE_GRP | 46-55 | 25609, 18% | 3206, 16% | 408, 18% | 277, 18% | 22122, 18% |
| AGE_GRP | 56-65 | 34359, 24% | 5058, 25% | 615, 27% | 427, 27% | 28910, 23% |
| AGE_GRP | 66<= | 48927, 34% | 7677, 39% | 760, 33% | 453, 29% | 40823, 33% |
| Hispanic | ||||||
| HISPANIC | N | 136047, 94% | 18643, 94% | 2170, 94% | 1454, 93% | 115993, 94% |
| HISPANIC | NI | 1070, 1% | 140, 1% | 20, 1% | 19, 1% | 917, 1% |
| HISPANIC | Y | 8232, 6% | 1055, 5% | 116, 5% | 88, 6% | 7096, 6% |
| Race | ||||||
| RACE | 01 | 538, 0% | 77, 0% | 15, 1% | 7, 0% | 450, 0% |
| RACE | 02 | 1415, 1% | 193, 1% | 23, 1% | 10, 1% | 1207, 1% |
| RACE | 03 | 21020, 14% | 3037, 15% | 380, 16% | 278, 18% | 17690, 14% |
| RACE | 04 | 108, 0% | 13, 0% | 5, 0% | 2, 0% | 92, 0% |
| RACE | 05 | 109233, 75% | 14607, 74% | 1651, 72% | 1091, 70% | 93591, 75% |
| RACE | 06 | 314, 0% | 36, 0% | 7, 0% | 5, 0% | 273, 0% |
| RACE | 07 | 509, 0% | 80, 0% | 5, 0% | 9, 1% | 425, 0% |
| RACE | NI | 165, 0% | 29, 0% | 6, 0% | 5, 0% | 132, 0% |
| RACE | OT | 12047, 8% | 1766, 9% | 214, 9% | 154, 10% | 10146, 8% |
| Sex | ||||||
| SEX | F | 72415, 50% | 8564, 43% | 1082, 47% | 547, 35% | 63156, 51% |
| SEX | M | 72932, 50% | 11274, 57% | 1224, 53% | 1014, 65% | 60848, 49% |
| SEX | NI | 2, 0% | 0, 0% | 0, 0% | 0, 0% | 2, 0% |
| Total | ||||||
| TOTAL | (%/overall) | 145349, 100% | 19838, 14% | 2306, 2% | 1561, 1% | 124006, 85% |
Demographic characterizations for patients at different AKI stages are summarized in Table 1.
Vital variables include: Height, Weight, BMI, Blood Pressure (Systolic, Diastolic), Smoking Status.
| days_from_admit | 1.encounters# | 2.records# | 3.low_records# | 4.high_records# | 5a.min | 5b.median | 5c.mean | 5d.sd | 5e.cov | 5f.max |
|---|---|---|---|---|---|---|---|---|---|---|
| BMI | ||||||||||
| 0> | 30904 | 40140 | 0 | 642 | 2 | 28 | 32 | 230 | 7.2 | 24857 |
| 1 | 137742 | 137742 | 0 | 2983 | 3 | 28 | 32 | 285 | 8.9 | 73000 |
| 2 | 6019 | 6019 | 0 | 122 | 2 | 28 | 30 | 10 | 0.3 | 287 |
| 3 | 4748 | 4748 | 0 | 64 | 12 | 28 | 29 | 9 | 0.3 | 242 |
| 4 | 3756 | 3756 | 0 | 51 | 13 | 28 | 29 | 8 | 0.3 | 174 |
| 5 | 2321 | 2321 | 0 | 36 | 14 | 28 | 30 | 9 | 0.3 | 213 |
| 6 | 2147 | 2147 | 0 | 36 | 13 | 28 | 30 | 13 | 0.4 | 442 |
| 7 | 2336 | 2336 | 0 | 41 | 14 | 29 | 30 | 8 | 0.3 | 105 |
| 7< | 13348 | 19426 | 0 | 283 | 4 | 29 | 31 | 140 | 4.5 | 19051 |
| overall | 140992 | 218635 | 0 | 4258 | 2 | 28 | 32 | 250 | 7.8 | 73000 |
| BP_DIASTOLIC | ||||||||||
| 0> | 40800 | 404763 | 2754 | 1006 | 0 | 70 | 71 | 14 | 0.2 | 232 |
| 1 | 115366 | 752487 | 8356 | 7856 | 0 | 73 | 74 | 17 | 0.2 | 235 |
| 2 | 109198 | 968666 | 10562 | 3314 | 0 | 69 | 70 | 15 | 0.2 | 236 |
| 3 | 110050 | 776417 | 7080 | 1741 | 0 | 69 | 70 | 15 | 0.2 | 220 |
| 4 | 95597 | 639243 | 5367 | 1435 | 1 | 69 | 70 | 15 | 0.2 | 206 |
| 5 | 80142 | 523840 | 4445 | 985 | 0 | 69 | 70 | 14 | 0.2 | 200 |
| 6 | 67215 | 434161 | 3758 | 779 | 5 | 69 | 70 | 14 | 0.2 | 209 |
| 7 | 57497 | 372107 | 3311 | 608 | 0 | 69 | 69 | 14 | 0.2 | 216 |
| 7< | 73764 | 3518467 | 38818 | 5384 | 0 | 68 | 69 | 14 | 0.2 | 237 |
| overall | 145308 | 8390151 | 84451 | 23108 | 0 | 69 | 70 | 15 | 0.2 | 237 |
| BP_SYSTOLIC | ||||||||||
| 0> | 40800 | 422493 | <11 | 378 | 34 | 124 | 126 | 21 | 0.2 | 467 |
| 1 | 115366 | 789136 | 17 | 4041 | 30 | 127 | 129 | 25 | 0.2 | 313 |
| 2 | 109198 | 1026881 | 16 | 936 | 0 | 123 | 125 | 22 | 0.2 | 310 |
| 3 | 110050 | 816349 | <11 | 417 | 0 | 123 | 125 | 21 | 0.2 | 285 |
| 4 | 95597 | 670250 | <11 | 335 | 31 | 124 | 125 | 21 | 0.2 | 306 |
| 5 | 80142 | 550025 | <11 | 264 | 31 | 124 | 126 | 21 | 0.2 | 292 |
| 6 | 67215 | 455833 | <11 | 199 | 31 | 124 | 125 | 21 | 0.2 | 315 |
| 7 | 57497 | 390751 | <11 | 168 | 0 | 124 | 125 | 21 | 0.2 | 260 |
| 7< | 73764 | 3707724 | 54 | 1371 | 0 | 123 | 124 | 20 | 0.2 | 467 |
| overall | 145308 | 8829442 | 119 | 8109 | 0 | 124 | 125 | 21 | 0.2 | 467 |
| HT | ||||||||||
| 0> | 29262 | 37044 | 0 | <11 | 2 | 67 | 67 | 4 | 0.1 | 97 |
| 1 | 131211 | 131211 | 0 | <11 | 1 | 67 | 67 | 5 | 0.1 | 113 |
| 2 | 5707 | 5707 | 0 | 0 | 24 | 67 | 67 | 4 | 0.1 | 83 |
| 3 | 4448 | 4448 | 0 | 0 | 24 | 67 | 67 | 4 | 0.1 | 81 |
| 4 | 3569 | 3569 | 0 | 0 | 34 | 67 | 67 | 4 | 0.1 | 81 |
| 5 | 2216 | 2216 | 0 | 0 | 27 | 67 | 67 | 4 | 0.1 | 81 |
| 6 | 2044 | 2044 | 0 | 0 | 18 | 67 | 67 | 4 | 0.1 | 80 |
| 7 | 2227 | 2227 | 0 | 0 | 48 | 67 | 67 | 4 | 0.1 | 81 |
| 7< | 12976 | 18722 | 0 | 0 | 6 | 67 | 67 | 4 | 0.1 | 83 |
| overall | 136713 | 207188 | 0 | <11 | 1 | 67 | 67 | 5 | 0.1 | 113 |
| WT | ||||||||||
| 0> | 31398 | 40693 | 0 | 330 | 16 | 179 | 186 | 52 | 0.3 | 705 |
| 1 | 141659 | 141659 | 0 | 1787 | 20 | 180 | 188 | 56 | 0.3 | 1105 |
| 2 | 6036 | 6036 | 0 | 72 | 12 | 180 | 188 | 56 | 0.3 | 1374 |
| 3 | 4768 | 4768 | 0 | 41 | 71 | 179 | 186 | 52 | 0.3 | 621 |
| 4 | 3765 | 3765 | 0 | 24 | 75 | 177 | 183 | 50 | 0.3 | 567 |
| 5 | 2332 | 2332 | 0 | 17 | 70 | 183 | 189 | 52 | 0.3 | 506 |
| 6 | 2153 | 2153 | 0 | 24 | 79 | 184 | 190 | 52 | 0.3 | 481 |
| 7 | 2341 | 2341 | 0 | 27 | 75 | 183 | 190 | 54 | 0.3 | 668 |
| 7< | 13373 | 19456 | 0 | 169 | 19 | 186 | 192 | 52 | 0.3 | 620 |
| overall | 144642 | 223203 | 0 | 2491 | 12 | 180 | 188 | 54 | 0.3 | 1374 |
Table 2a identifies extreme values of vitals for height, weight, BMI, and blood pressure, which may suggest systemic errors such as typos, and conversion mistakes.
| key_cat | 1.patients# | 2.encounters# | 3.encounters% |
|---|---|---|---|
| SMOKING | |||
| SMOKING_01,02 | 92 | 94 | 0% |
| SMOKING_01,03 | 538 | 562 | 0% |
| SMOKING_NI | 39617 | 52103 | 36% |
| SMOKING_03 | 23931 | 37139 | 26% |
| SMOKING_08 | 295 | 360 | 0% |
| SMOKING_02,03 | 349 | 357 | 0% |
| SMOKING_03,08 | 33 | 34 | 0% |
| SMOKING_02 | 21703 | 33608 | 23% |
| SMOKING_03,06 | 32 | 33 | 0% |
| SMOKING_02,06 | 25 | 26 | 0% |
| SMOKING_01 | 14517 | 20508 | 14% |
| SMOKING_06 | 188 | 196 | 0% |
| SMOKING_01,06 | 16 | 16 | 0% |
| SMOKING_05 | 114 | 143 | 0% |
| SMOKING_02,08 | 13 | 14 | 0% |
| SMOKING_07 | 87 | 112 | 0% |
| TOBACCO | |||
| TOBACCO_NI | 53016 | 71983 | 50% |
| TOBACCO_02 | 41722 | 66927 | 46% |
| TOBACCO_01,03 | 53 | 54 | 0% |
| TOBACCO_01,02 | 43 | 44 | 0% |
| TOBACCO_03 | 2643 | 4018 | 3% |
| TOBACCO_01 | 1590 | 2167 | 1% |
| TOBACCO_02,03 | 152 | 155 | 0% |
| TOBACCO_TYPE | |||
| TOBACCO_TYPE_02 | 554 | 761 | 1% |
| TOBACCO_TYPE_NI | 53057 | 72053 | 50% |
| TOBACCO_TYPE_01,04 | 623 | 644 | 0% |
| TOBACCO_TYPE_01 | 28159 | 43606 | 30% |
| TOBACCO_TYPE_03,04 | 32 | 33 | 0% |
| TOBACCO_TYPE_02,04 | 31 | 31 | 0% |
| TOBACCO_TYPE_04 | 16882 | 26780 | 18% |
| TOBACCO_TYPE_01,03 | 21 | 22 | 0% |
| TOBACCO_TYPE_02,03 | 20 | 21 | 0% |
| TOBACCO_TYPE_03 | 1046 | 1375 | 1% |
Table 2b identifies unreliable reporting of smoking status. A significant mismatch between smoking and tabacco summaries needs some further investigation.
A total of 817 LOINC identifiable labs are eligible (NI may present), among which 579 are collected at the day of admission, 773 within 3 days. Figure 1 shows the data density and intensity of labs concepts, which can help identify common labs (e.g. the common labs for this study cohort are 2160-0, NI,17861-6,1963-8,2075-0,…), and labs with very high recording intensity (e.g. NI, [lab_report$key[7]], [lab_report$key[8]]).
A Total of 280 distinct CCS-grouped diagnoses has been assigned to patients before the encounter of interest. Figure 2 gives an overview of average history of patients’ diagnosis prior to tne encounter of interest as well as the highly frequent historical diagnoses(e.g. 259(Residual codes; unclassified), 257(Other aftercare), 133(Other lower respiratory disease), 95(Other nervous system disorders), 155(Other gastrointestinal disorders), 258(Other screening for suspected conditions (not mental disorders or infectious disease))).
There are 249 distcint CCS-group admission diagnoses for encounters of interest. Figure 3 layouts the ditribution of the admission diagnoses associated with patients’ baseline serum creatinine characteristics (e.g. 259(Abdominal pain), 133(Other lower respiratory disease), 106(Cardiac dysrhythmias) are the most common diagnosis for this study cohort; while patients admitted due to 119(Varicose veins of lower extremity), 157(Acute and unspecified renal failure), 158(Chronic kidney disease ) tends to have lower average baseline SCr.
A Total of 15951 distinct total procedures codes have been assigned to patients before the encounter of interest. Figure3 gives an overview of average history of patients’ procedures prior to tne encounter of interest as well as the highly frequent historical procedures they had recieved. It can help identify the common procedures or typical occuring times of precedures (e.g. CH:99285, CH:80053,CH:85025,CH:85025,CH:85025,…). Note that Figure2 and Figure3 may display similar distributions as a result of corrlations between diagnoses and procedures.
note: links for cpt codes may lead to invalid page
A Total of 11539 distinct RXNORM medication concepts are discovered for the cohort. Figure 5 demonstrates average exposures for drug starting at 1st, 2nd, 3rd,…, 7th and after 7th days since admission. It helps identify typical medciations dispensed (:01) or administered(:02) during the course of stay. (e.g. the typical medications identified are 1807627(150 ML Sodium Chloride 9 MG/ML Injection), 1807627(150 ML Sodium Chloride 9 MG/ML Injection), 1807627(150 ML Sodium Chloride 9 MG/ML Injection); while 1807627(150 ML Sodium Chloride 9 MG/ML Injection), 1807627(150 ML Sodium Chloride 9 MG/ML Injection), 1807627(150 ML Sodium Chloride 9 MG/ML Injection) seems to be exposed for relatively long period on average).